Portrait of Ganesh Ananthanarayanan

Ganesh Ananthanarayanan



I am a Researcher in the Mobility and Networking group at Microsoft Research.

I finished my Ph.D. in the AMP Lab at Berkeley in Dec 2013, advised by Ion Stoica.

I’m interested in all aspects of systems and networking. My current research focus:

  • Video Processing: Cameras are everywhere! Large-scale video processing is a grand challenge representing an important frontier. With the rising popularity of Internet of Things videos from factory floors, traffic and police, and retail shops, cameras represent the most challenging of the “things” in terms of data volume, (vision) processing algorithms, response latencies, and security sensitivities. [NSDI’17]
  • Internet Performance – Bringing Big Data to Networking: Companies like Microsoft have multiple services with worldwide clients that continuously measure performance. Using this large scale data, orders of magnitude more than prior studies, we seek to better understand network performance and create a continuous performance map of the Internet. [SIGCOMM’16][PinDrop]
  • Geo-distributed Analytics: The next generation of big data analytics will no longer be confined to one datacenter but happen across multiple datacenters, edge clusters etc. How will the infrastructure for geo-distributed analytics look like? [OSDI’16-a] [SIGCOMM’15-a]
  • Datacenter Resource Management: Resource management is critical for large scale clusters executing complex computations. We design scheduling solutions – straggler mitigation, fairness, packing, etc. – based on theoretically-founded principles while considering multiple resources (CPU, memory, disk, network). [OSDI’16-b][SIGCOMM’15-b][OSDI’14][SIGCOMM’14][NSDI’14]

Selected Publications:

Live Video Analytics at Scale with Approximation and Delay-Tolerance
H. Zhang, G. Ananthanarayanan, P. Bodik, M. Philipose, P. Bahl, M. J. Freedman
USENIX NSDI, Boston, MA, Mar 2017.

Via: Improving Internet Telephony Call Quality Using Predictive Relay Selection 
J. Jiang, R. Das, G. Ananthanarayanan, P. A. Chou, V. N. Padmanabhan, V. Sekar, E. Dominique, M. Goliszewski, D. Kukoleca, R. Vafin, H. Zhang
ACM SIGCOMM, Florianopolis, Brazil, Aug 2016.

Low Latency Geo-distributed Data Analytics
Q. Pu, G. Ananthanarayanan, P. Bodik, S. Kandula, A. Akella, P. Bahl, I. Stoica
ACM SIGCOMM, London, UK, Aug 2015.


Live Video Analytics

With the rising popularity of the Internet of Things (IoT), cameras are now everywhere. Large-scale video processing is a grand challenge representing an important frontier, what with videos from factory floors, traffic, police, and retail shops representing increasing data volumes, the need for better vision-processing algorithms, issues with response latencies, and security sensitivities. This project’s goal is to build a system for low-cost, accurate analysis of live videos. This system will work across a geodistributed hierarchy of…

Edge Computing

Established: December 6, 2015

News Stories Network World, Microsoft Researcher: Why Micro Datacenters Really Matter to Mobile's Future, September 3, 2015 (also Computer World, IT World, Info World) Shepherd's PI, Why a Cloudlet Beats the Cloud for Mobile Apps, by Lewis Sheppherd, December 13, 2009 Keynote Presentations Cloud 2020: Emergence of Micro Data Centers (Cloudlets) for Latency Sensitive Computing Devices and Networking Summit 2015, Paris, France, May 13, 2015 IEEE Wireless Communications and Networking Conference (WCNC) 2015, New Orleans,…


Established: May 28, 2015

The PinDrop project focuses on building the substrate for supporting high-quality real-time streaming over wired and wireless networks. Context Real-time streaming across the wide-area network (WAN) is key to several existing and anticipated services, including voice and video conferencing (e.g., Skype, Viber), remote console access (e.g., remote desktop, VNC), remote application streaming (e.g., Azure RemoteApp), and cloud-based gaming. A common characteristic of these services is bidirectional interactive communication, between two or more users or between a user…















  • Taming the Video Star! Real-time Video Analytics at Scale
    Univ. of California, Berkeley, May 2017
    Cornell University, Apr 2017
  • Geo-distributed Data Analytics [pptx]
    Univ. of Wisconsin, Madison, Oct 2015
  • Big Data Analytics with Parallel Jobs [pptx]
    Univ. of Illinois, Urbana-Champaign, Mar 2013
  • Effective Straggler Mitigation: Attack of the Clones [pptx]
    AMPLab Summer Retreat, May 2012 and Hortonworks Inc., Nov 2012
  • PACMan: Coordinated Memory Caching for Parallel Jobs [pptx]
    Intel ISTC-CC Retreat, Nov 2012, and VMWare Inc., May 2012
  • Coordinated In-Memory Caching for Data Intensive Clusters [pptx]
    Yahoo! Research, Jun 2011 and Huawei Technologies Co. Ltd., Jul 2011
  • Disk-Locality in Datacenter Computing Considered Irrelevant [pptx]
    USENIX HotOS, May 2011 and Berkeley Cloud Seminar, Feb 2011
  • Scarlett: Coping with Skewed Popularity Content in MapReduce Clusters [pptx]
    Hortonworks Inc., Jul 2011, Cloudera Inc., Apr 2011, and Facebook Inc., Nov 2010
  • Reining in the Outliers in MapReduce Clusters using Mantri [pptx]
    Yahoo! Research, Nov 2010